_Etsy is the global marketplace for unique and creative goods. We build, power, and evolve the tools and technologies that connect entrepreneurs with buyers around the world. As an Etsy employee, you’ll tackle unique problems alongside talented coworkers committed to Keeping Commerce Human. We're large enough that you'll focus on meaningful, complex challenges, but small enough that you can make a rewarding impact

About The Team

The Risk Decisioning team keeps Etsy a safe and trusted marketplace by building advanced and scalable ML technologies to detect and prevent risk and fraud. We currently have 8 engineers on the team, with a mix of ML Engineers, Applied Scientists, and Full Stack Engineers.

About The Role

We are looking for someone who is passionate about applying ML to deliver customer impact and have the domain expertise and vision to serve as the team’s technical leader in ML. Our work addresses pressing, real-world problems, including detection of transactional fraud, fake account creation, collusion fraud, and more. As a member of the engineering team, this role is a great opportunity to collaborate with Senior ML professionals on large-scale projects protecting millions of users.

What does the day-to-day look like?

Solve customer and business problems (protecting against seller fraud, transactional fraud, account takeover, fake accounts, etc.) using machine learning techniques like graph ML, deep neural networks, and anomaly detection.
Take ideas and scale them to millions to users. Develop and implement end-to-end plans, including idea generation, project planning, model development, production model serving, and ownership of model performance.
Architect new and improve on existing ML systems, including building data architectures (e.g. large-scale graph data processing and storage), enabling faster and more robust model development, and improving model serving and orchestration.
Provide technical leadership on long-term strategy, roadmap, ML design, and system architecture.
Help to coach and mentor more junior team members.
Collaborate with team members and cross-team partners for problem identification, technical design/delivery, and product operationalization.
Share impactful and innovative work in the wider ML research community, including presenting at top-tier ML conferences such as: KDD, ICML, NeurIPS, etc.
Of course, this is just a sample of the kinds of work this role will require! You should assume that your role will encompass other tasks, too, and that your job duties and responsibilities may change from time to time at Etsy's discretion, or otherwise applicable with local law.

Of course, this is just a sample of the work the role will require. You should assume that the role will encompass other tasks, too, and that the job duties and responsibilities may change from time to time at Etsy's discretion, or otherwise applicable with local law.

Qualities that will help you thrive in this role are:

You have 3+ years experience building ML models in the fraud/risk management space.
Python programing skills for machine learning applications in the risk/fraud domain
Experience deploying, debugging, and fine-tuning machine learning models in large-scale production systems in public clouds, with experience in Infrastructure as Code.
You are comfortable with using git, Linux environments, dockers, and other tools for writing robust, production-ready code.
You have focused experience deploying models in production at scale like unsupervised anomaly detection, graph neural network, deep learning, natural language processing, or reinforcement learning.

Nice-to-Haves

Google Cloud Platform (GCP) experience is a plus

Hours and Location

Monday - Friday, 40 hours/week
100% Remote - US

_We offer a competitive salary range for this position. Most candidates who join our team are hired at the median of this range, ensuring fair and equitable compensation based on experience and qualifications.

_An Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.